The Exploration of Artificial Intelligence in Pronunciation Teaching

Yian Zhao

Computer science, American University, 20007, Washington, U.S.A.

Keywords: Artificial Intelligence, Pronunciation Education, Machine Learning.

Abstract: This paper explores the integration of Artificial Intelligence (AI) in pronunciation education, shedding light

on its transformative impact, limitations, and future direction. It examines how AI's adoption, through

technologies like speech recognition and adaptive learning algorithms, offers personalized and accessible

learning paths, significantly enhancing pronunciation skills. The study highlights AI's capacity for real-time

feedback and its role in creating immersive learning experiences via augmented and virtual reality, thereby

revolutionizing traditional language learning methodologies. Addressing ethical considerations, the paper

delves into data privacy and the challenges of ensuring unbiased AI systems. It acknowledges the limitations

of AI, such as its lack of emotional intelligence and the potential for decreased human interaction,

emphasizing the necessity of a balanced approach that marries technological innovation with the irreplaceable

value of human touch in education. By proposing a forward-looking perspective, the research advocates for

further exploration into hybrid models that integrate AI tools with conventional teaching methods, aiming to

optimize language learning outcomes. This study contributes to the broader discourse on educational

technology by providing insights into the responsible and effective use of AI in pronunciation education,

underlining the importance of ethical considerations and the continued need for human-centered educational

practices.

1 INTRODUCTION

The intersection of Artificial Intelligence (AI) with

language education, particularly in the field of

pronunciation learning, represents a newly emerged

subject of research. It provides profound implications

with international students who face linguistic

barriers in foreign academic environments.

Pronunciation is an unavoidable problem due to the

growing number of students studying abroad. They

frequently have difficulty acquiring the correct

pronunciation of a new language, impacting their

academic and social integration. Conventional

approaches to teaching pronunciation which are

limited by low exposure to native speech patterns and

a lack of personalized feedback, often fail to address

the specific requirementbus of these students. The

emergence of AI in this area offers an optimistic

resolution, providing ways for customized and

adaptable learning experiences designed to suit the

varied language backgrounds of international

students.

Some progress has been made in applying AI in

pronunciation education, which is transforming the

traditional frameworks of language learning. These

advancements include sophisticated speech

recognition technologies, Natural Language

Processing (NLP) algorithms, and adaptive learning

algorithms. Improved speech recognition

technologies ensure more accurate inputs while NLP

investigates the interactions between humans and

computers via natural language. The field focuses on

fundamental technologies related to the interpretation

of meaning and semantic processing, including

Machine Translation (MT) and Question-Answering

(QA) (Zhou 2021). Besides, adaptive learning is an

approach that assesses students' knowledge levels and

learning preferences and adjusts materials, tasks, and

delivery methods to meet the specific needs of the

learners (Morze 2017). This technical advancement is

especially advantageous for international students,

who struggle with pronunciation difficulties during

daily conversation.

Moreover, the integration of AI into

pronunciation education gives opportunities for

customized educational processes. In this way,

international students are provided with study

modules specifically designed to target pronunciation

Zhao, Y.

The Exploration of Artiﬁcial Intelligence in Pronunciation Teaching.

DOI: 10.5220/0012829100004547

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st Inter national Conference on Data Science and Engineering (ICDSE 2024), pages 127-131

ISBN: 978-989-758-690-3

127

difficulties associated with native languages. This

individualized method encompasses not only the

corrections of pronunciation but also building

confidence and reducing anxiety in a foreign land. In

Noviyanti’s research, an artificial intelligence-based

pronunciation checker is proven to enhance higher

education students’ oral speaking grades and ability

(Noviyanti 2020).

The purpose of this study is to analyze those

advancements and assess their practical application in

the field of pronunciation education for international

students. It includes an analysis of how AI-driven

tools can identify and modify pronunciation errors,

offer real-time feedback, and track progress over

time. Another crucial aspect of this study involves

evaluating the extent to which these AI technologies

provide a more immersive and interactive learning

experience, and compare it to conventional

techniques of pronunciation methods.

The outline of the paper is organized as follows.

In the first place, the paper will discuss the

technological foundations of AI tools in

pronunciation education, emphasizing their

application in the context of international students in

detail. The subsequent sections assess the efficacy of

various tools in improving pronunciation abilities

within this specific demographic, based on other

actual-data research, case examples, and user

feedback. Then it will focus on analyzing the

challenges encountered by international students

when using AI tools, including the limitations of AI

tools, and the balance between the use of AI

technology and human interaction. The final section

considers the future path of AI in pronunciation

education for international students, promoting

continuous innovation and practices to improve their

learning experience and daily talks.

2 METHOD

2.1 Framework of AI in Improving

Pronunciation

The framework for AI in pronunciation education

should be systematic, starting with the collection of

comprehensive pronunciation datasets. These

datasets serve as a foundation for the AI model and

are thoroughly compiled, encompassing a wide range

of accents, and dialects. The next crucial phase is the

building of the AI model, which includes advanced

algorithms for analyzing, recognizing, and correcting

pronunciation. This stage involves the integration of

sophisticated speech recognition and NLP

capabilities. Training the AI model is an over and

over again process, where it is exposed to various

pronunciation patterns to learn and adapt. In the final

stage, the artificial intelligence model is subjected to

strict and severe testing, during which its

performance in actual-life situations is evaluated. A

sample architecture for pronunciation recognition is

provided in Fig. 1.

Figure 1: A sample architecture for pronunciation recognition (Zhu 2021).

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2.2 Pronunciation Error Detection

2.2.1 Computer Assisted Language

Learning (CAPT) System

The method uses both language skills and up-to-date

speaking technology. The HMM classifier, which

was taught with linguists' annotations, can not only

tell the difference between right and wrong

phonemes, but it can also tell how badly a wrong

phoneme is pronounced. In the CAPT system, Ai uses

the Hidden Markov Model Toolkit (HTK) to teach a

language model how to recognize phonemes (Ai

2015). To get ready for the training, mistakes that

annotators find are categorized. The trained model,

the modified dictionary, the generated grammar, and

the extracted features are used with HTK to recognize

phonemes. The recognition result is a string of

phonemes, which is then compared to the right string

of phonemes made by the MARY phonemicized. If

they are the same, the learner's speech is correct. If

they are different, the difference between the two

sequences makes it easy to spot possible

pronunciation mistakes.

2.2.2 Hidden Markov Model (HMM)

According to Liu and Quan, this way of judging

pronunciation mistakes is based on the normal level

of standard speech (Liu & Quan 2022). It looks at

things like speed, pronunciation, semantics, and more

when judging how words are used and how they are

pronounced. In the HMM modeling method (a sample

architecture is shown in Fig. 2) for speech

recognition, the Viterbi algorithm and the improved

posterior probability algorithm are used to

automatically understand what students are saying.

Figure 2: A sample architecture for HMM (Christopher

2020).

2.3 Pronunciation Improvement

2.3.1 Chatbot AI

Advanced natural language processing algorithms

help these chatbots understand and reply to user input

in a way that feels like talking to a real person.

Mission Fluent is a new tool that helps people

improve their English pronunciation by giving them

drills and real-time tests that are designed to improve

pronunciation. In Hoang et al. study, they examine

how chatbot AI improves English pronunciation in

first-year vocational students in two Hanoi college

English classrooms (Hoang et al 2023). Level 1.1

mixed-level classrooms (A1 on the CEFR scale)

include 33 to 38 pupils. The study uses online video

courses and in-person sessions to prepare for a final

speaking test that emphasizes pronunciation. A quasi-

experimental approach divides students into two

groups: an experimental group getting chatbot AI

interaction and a control group continuing formal

pronunciation instruction. To assess intervention

efficacy, both groups read a piece and are rated by an

AI chatbot before and after the exam. To assess

participants' attitudes, opinions, and satisfaction with

the chatbot AI, as well as their experiences and

problems, Google Forms surveys and semi-structured

interviews are used. Student identities are designated

A1–A30 for privacy. The study examines how

chatbot AI improves English pronunciation in

vocational education using correlation, inferential,

descriptive, and qualitative data analysis.

2.3.2 Artificial Intelligence-Based

Pronunciation Checker

A mixed-methods technique was used to find out how

well a spell checker helped students improve their

pronunciation. A one-group pretest-posttest design

was used to collect quantitative data to figure out how

the tool improved pronunciation. At the same time, a

questionnaire was used to collect qualitative data

about how students felt about the program. Purposive

sampling was used to choose the thirty students who

took part in the study. Ten were male and twenty were

female. For the quantitative part, a speech test was

given to see how well the spell checker worked by

checking how accurately the students pronounced

words. A twelve-question questionnaire with "agree"

or "disagree" options based on previous related

literature was used to collect the qualitative data. The

reliability score for the questionnaire was 0.075,

which means it was moderately reliable and could be

used in this study. Descriptive analysis was used on

The Exploration of Artiﬁcial Intelligence in Pronunciation Teaching

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both the pronunciation test and the interview to find

out how well the spell checker helped students

improve their pronunciation.

3 DISCUSSION

When it comes to teaching pronunciation, the

introduction of artificial intelligence has opened up a

whole new world of possibilities. Some AI

technology has already been put into use in the

practical application. According to Hoang et al.,

MissionFluent is a published chatbot that can

effectively enhance the English pronunciation

abilities of students (Hoang et al. 2023). Besides, it

facilitates continuous and immediate feedback, an

essential element in language acquisition. According

to this perspective, AI has a bright future in the field

of education. Pronunciation practice will probably

become more entertaining and engaging with the

advent of future AI systems that provide more

interactive and enhanced learning experiences and

this can significantly enhance learner motivation and

retention rates. On the other hand, the use of AI in

pronunciation instruction is expected to increase

accessibility and inclusivity. AI-driven pronunciation

tools have the potential to reach a larger audience,

including individuals residing in rural or

underdeveloped places, due to the ongoing decrease

in technological costs and the rise in internet usage

worldwide. However, there are still a lot of

limitations when using AI to learn pronunciation. In

the first place, the accuracy of the algorithms and the

data utilized to train these systems have a significant

impact on how effective AI is at teaching

pronunciation. Limited or biased training datasets can

introduce biases into AI, which could result in

pronunciation models that are not correct (Jiang &

Nachum 2020, Ghani et al. 2023). If the speech

patterns of learners from varied language origins are

not well-represented in the training data, these biases

can be particularly damaging to them. Additionally,

while AI can assess and correct speech, it still has a

limited grasp of language usage and context when

compared to a human teacher. The subtleties of

language that are influenced by situational or cultural

settings may be difficult for AI to understand. Last

but not least, for AI systems to work well, large

volumes of personal data—including voice

recordings—are needed. Given the sensitive nature of

biometric data, such as voice patterns, this creates

privacy and security concerns. More advanced

technologies should be considered in the future (Qiu

et al. 2022).

4 CONCLUSION

This paper provides an in-depth examination of AI's

role in pronunciation teaching, exploring its

advantages, limitations, and future prospects. It

emphasizes how AI technologies like speech

recognition, natural language processing, and

adaptive learning have revolutionized pronunciation

instruction, making language learning more

personalized, immediate, and accessible. Through a

detailed analysis, the paper highlights AI's capacity to

tailor learning experiences, meet diverse linguistic

needs, and offer instant feedback, significantly

enhancing pronunciation practice's effectiveness. It

also delves into innovative methods such as

integrating AI with virtual and augmented reality for

immersive language learning experiences.

Moreover, the essay addresses crucial ethical

considerations, including data privacy and the

necessity for unbiased algorithms, advocating for a

more thoughtful and responsible AI application in

education. It contributes to broader discussions on

AI's educational role, urging a balanced approach that

respects both technological innovation and essential

human aspects of teaching and learning.

However, the research acknowledges its

limitations, particularly the scarcity of sample data

and studies, which constrains its conclusions'

precision. It suggests future research directions,

including examining AI's impact on emotional

intelligence in language learning, its long-term

effects across different socio-economic and cultural

contexts, and the development of hybrid models that

combine AI with traditional teaching methods.

In summary, the essay highlights significant

progress in AI-enhanced pronunciation instruction

and calls for a comprehensive strategy that maximizes

AI's benefits while adhering to ethical standards and

educational objectives. This approach aims to create

a more effective and inclusive learning environment

worldwide, carefully navigating AI's possibilities and

challenges as it becomes increasingly integral to

education.

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